Towards a Learned Cost Model for Distributed Spatial Join: Data, Code & Models

Towards a Learned Cost Model for Distributed Spatial Join: Data, Code & Models
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面向分布式空间连接的学习成本模型:数据、代码

DOI:
10.1145/3511808.3557712
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发表时间:
2022
期刊:
ACM
影响因子:
--
通讯作者:
Eldawy, Ahmed
Eldawy, Ahmed
中科院分区:
--
文献类型:
--
作者:
Vu, Tin;Belussi, Alberto;Migliorini, Sara;Eldawy, Ahmed

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地理空间数据约占所有公开可用数据的60%。将多个地理空间数据集集合在一起的最基本和最复杂的操作之一是空间连接操作。由于空间连接问题的复杂性,空间连接问题的划分技术和并行算法有很多。这导致了一个复杂的查询优化问题:对于我们想要连接的给定输入数据集对,使用哪种算法?随着机器学习的兴起,通过使用各种学习模型来解决这个问题是有希望的。然而,一个令人担忧的问题是,缺乏可供培训和测试的标准和公开可用的数据,以及缺乏可访问的基准模型。这篇资源白皮书通过提供用于空间连接的合成和真实数据集、用于构建更多数据集的源代码以及研究人员可以进一步扩展和比较的几个基线解决方案来帮助研究界解决这一问题。
Geospatial data comprise around 60% of all the publicly available data. One of the essential and most complex operations that brings together multiple geospatial datasets is the spatial join operation. Due to its complexity, there is a lot of partitioning techniques and parallel algorithms for the spatial join problem. This leads to a complex query optimization problem: which algorithm to use for a given pair of input datasets that we want to join? With the rise of machine learning, there is a promise in addressing this problem with the use of various learned models. However, one of the concerns is the lack of a standard and publicly available data to train and test on, as well as the lack of accessible baseline models. This resource paper helps the research community to solve this problem by providing synthetic and real datasets for spatial join, source code for constructing more datasets, and several baseline solutions that researchers can further extend and compare to.
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